
import pandas as pd
import numpy as np
import plotly.graph_objs as go
import matplotlib.pyplot as plt
from plotly.offline import iplot
import seaborn as sns
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_samples
from sklearn.metrics import silhouette_score
from mpl_toolkits.mplot3d import Axes3D
from sklearn.preprocessing import OneHotEncoder
from sklearn.model_selection import train_test_split
import keras
from keras.models import Sequential
from keras.layers import Dense
from sklearn.metrics import accuracy_score
import ipywidgets as widgets
from keras.models import load_model
from ipywidgets import *

print("Libraries loaded!")

Libraries loaded!


# !jupyter nbextension enable – py widgetsnbextension – sys-prefix
# !jupyter serverextension enable voila – sys-prefix


print()
print("Loading data.....")
customer_data = pd.read_csv('E-Commerce_Data.csv', encoding='ISO-8859-1',dtype={'InvoiceID': str})
customer_data['InvoiceDate'] = pd.to_datetime(customer_data['InvoiceDate']) #convert to python datetime object
print("Data loaded!")

Loading data.....
Data loaded!


print()
def check_data(dataframe):
    print(" *********************************SHAPE******************************")
    print(dataframe.shape)
    print()
    print()
    print("*********************************COLUMNS******************************")
    print(dataframe.columns)
    print()
    print()
    print("**********************************TYPES*******************************")
    print(dataframe.dtypes)
    print()
    print()
    print("**********************************HEAD*******************************")
    print(dataframe.head())
    print()
    print()
    print("**********************************TAIL*******************************")
    print(dataframe.tail())
    print()
    print()
    print("*******************************DESCRIPTION***************************")
    print(dataframe.describe().T)
    print()
    
    
check_data(customer_data)

 *********************************SHAPE******************************
(541909, 8)


*********************************COLUMNS******************************
Index(['InvoiceNo', 'StockCode', 'Description', 'Quantity', 'InvoiceDate',
       'UnitPrice', 'CustomerID', 'Country'],
      dtype='object')


**********************************TYPES*******************************
InvoiceNo              object
StockCode              object
Description            object
Quantity                int64
InvoiceDate    datetime64[ns]
UnitPrice             float64
CustomerID            float64
Country                object
dtype: object


**********************************HEAD*******************************
  InvoiceNo StockCode                          Description  Quantity  \
0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   
1    536365     71053                  WHITE METAL LANTERN         6   
2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   
3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   
4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   

          InvoiceDate  UnitPrice  CustomerID         Country  
0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom  
1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  
2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom  
3 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  
4 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  


**********************************TAIL*******************************
       InvoiceNo StockCode                      Description  Quantity  \
541904    581587     22613      PACK OF 20 SPACEBOY NAPKINS        12   
541905    581587     22899     CHILDREN'S APRON DOLLY GIRL          6   
541906    581587     23254    CHILDRENS CUTLERY DOLLY GIRL          4   
541907    581587     23255  CHILDRENS CUTLERY CIRCUS PARADE         4   
541908    581587     22138    BAKING SET 9 PIECE RETROSPOT          3   

               InvoiceDate  UnitPrice  CustomerID Country  
541904 2011-12-09 12:50:00       0.85     12680.0  France  
541905 2011-12-09 12:50:00       2.10     12680.0  France  
541906 2011-12-09 12:50:00       4.15     12680.0  France  
541907 2011-12-09 12:50:00       4.15     12680.0  France  
541908 2011-12-09 12:50:00       4.95     12680.0  France  


*******************************DESCRIPTION***************************
               count          mean          std       min       25%       50%  \
Quantity    541909.0      9.552250   218.081158 -80995.00      1.00      3.00   
UnitPrice   541909.0      4.611114    96.759853 -11062.06      1.25      2.08   
CustomerID  406829.0  15287.690570  1713.600303  12346.00  13953.00  15152.00   

                 75%      max  
Quantity       10.00  80995.0  
UnitPrice       4.13  38970.0  
CustomerID  16791.00  18287.0


#Get the total number of missing values for each attribute
print()
print(customer_data.isnull().sum())
print()

InvoiceNo           0
StockCode           0
Description      1454
Quantity            0
InvoiceDate         0
UnitPrice           0
CustomerID     135080
Country             0
dtype: int64


#Check for repeated rows
print()
print("Duplicated values:", customer_data.duplicated().sum())
print()

Duplicated values: 5268


#Get the unique number of countries within the data set
print()
temp = customer_data.groupby(['CustomerID', 'InvoiceNo', 'Country']).count()
#temp = customer_data.groupby(['Country']).count()
temp = temp.reset_index(drop = False)
countries = temp['Country'].value_counts()
print('Number of countries in the dataframe: {}' .format(len(countries)))

Number of countries in the dataframe: 37


#Visualize the total number of orders for different countries
data = dict(type='choropleth',
locations = countries.index,
locationmode = 'country names', z = countries,
text = countries.index, colorbar = {'title':'Order nb.'},
colorscale=[[0, 'rgb(224,255,255)'],
            [0.01, 'rgb(166,206,227)'], [0.02, 'rgb(31,120,180)'],
            [0.03, 'rgb(178,223,138)'], [0.05, 'rgb(51,160,44)'],
            [0.10, 'rgb(251,154,153)'], [0.20, 'rgb(255,255,0)'],
            [1, 'rgb(227,26,28)']],    
reversescale = False)
#_______________________
layout = dict(title='Number of orders per country',
geo = dict(showframe = True, projection={'type':'mercator'}))
#______________
choromap = go.Figure(data = [data], layout = layout)
iplot(choromap, validate=False)
print()


#Total number of customers, products and transactions
pd.DataFrame([{'products': len(customer_data['StockCode'].value_counts()),    
               'transactions': len(customer_data['InvoiceNo'].value_counts()),
               'customers': len(customer_data['CustomerID'].value_counts()),  
              }], columns = ['products', 'transactions', 'customers'], index = ['quantity'])


#Number of products purchased in every transaction
print()
temp = customer_data.groupby(by=['CustomerID', 'InvoiceNo'], as_index=False)['InvoiceDate'].count()
no_products_per_basket = temp.rename(columns = {'InvoiceDate':'Number of products'})
no_products_per_basket[:10].sort_values('CustomerID')


particular_code_list = customer_data[customer_data['StockCode'].str.contains(
    '^[a-zA-Z]+', regex=True)]['StockCode'].unique()
particular_code_list

array(['POST', 'D', 'C2', 'DOT', 'M', 'BANK CHARGES', 'S', 'AMAZONFEE',
       'DCGS0076', 'DCGS0003', 'gift_0001_40', 'DCGS0070', 'm',
       'gift_0001_50', 'gift_0001_30', 'gift_0001_20', 'DCGS0055',
       'DCGS0072', 'DCGS0074', 'DCGS0069', 'DCGS0057', 'DCGSSBOY',
       'DCGSSGIRL', 'gift_0001_10', 'PADS', 'DCGS0004', 'DCGS0073',
       'DCGS0071', 'DCGS0068', 'DCGS0067', 'DCGS0066P', 'B', 'CRUK'],
      dtype=object)


particular_code_description = customer_data[customer_data['StockCode'] == 'B']
particular_code_description


#Fill the missing values
customer_data['CustomerID'].fillna(inplace = True,value=customer_data['CustomerID'].mean())
customer_data['Description'].fillna(inplace = True, value='')
print("Done!")

Done!


#Check if there are still missing values
customer_data.isnull().sum()

InvoiceNo      0
StockCode      0
Description    0
Quantity       0
InvoiceDate    0
UnitPrice      0
CustomerID     0
Country        0
dtype: int64


#remove duplicated rows
customer_data.drop_duplicates(inplace=True)
print("Done!")

Done!


#Check if there are still duplicates
print("Repeated entries:",customer_data.duplicated().sum())

Repeated entries: 0


print("Length of data set now", len(customer_data))

Length of data set now 536641


#Gather all orders that might indicate a cancelled order
cancelledOrders=customer_data[customer_data["Quantity"]<0]
cancelledOrders.head()


#Check if negative quantity correspond to cancelled transaction
display(customer_data.sort_values('CustomerID')[:5])


#Inspect if all orders with negative quantity indicate cancelled orders
data_check = customer_data[customer_data['Quantity']<0][['CustomerID', 'Quantity', 'StockCode', 'Description', 'UnitPrice']]
for index, col in data_check.iterrows():
    if customer_data[(customer_data['CustomerID'] == col[0]) & (customer_data['Quantity'] == -col[1]) & 
            (customer_data['Description'] == col[2])].shape[0] == 0:
        print(data_check.loc[index])
        print('Hypothesis NOT fulfilled')
        break

CustomerID      14527.0
Quantity             -1
StockCode             D
Description    Discount
UnitPrice          27.5
Name: 141, dtype: object
Hypothesis NOT fulfilled


#Perform the previous check but ignore discount. 
data_check = customer_data[(customer_data['Quantity']<0) & (customer_data['Description'] != 'Discount')][['CustomerID', 'Quantity', 'StockCode', 'Description', 'UnitPrice']]
for index, col in data_check.iterrows():
    if customer_data[(customer_data['CustomerID'] == col[0]) & (customer_data['Quantity'] == -col[1]) & 
            (customer_data['Description'] == col[2])].shape[0] == 0:
        print(data_check.loc[index])
        print('HYPOTHESIS not fulfilled')
        break

CustomerID                             15311.0
Quantity                                    -1
StockCode                               35004C
Description    SET OF 3 COLOURED  FLYING DUCKS
UnitPrice                                 4.65
Name: 154, dtype: object
HYPOTHESIS not fulfilled


#Gather all entries that relate to cancelled orders and store the quantity cancelled for each cancelled order
data_cleaned =  customer_data.copy(deep =True)
data_cleaned['QuantityCanceled'] = 0

entry_to_remove = []
doubtful_entry = []

for index, col in customer_data.iterrows():
    if (col['Quantity']> 0) or col['Description'] == 'Discount': 
        continue
    data_test = customer_data[(customer_data['CustomerID'] == col['CustomerID']) & (customer_data['StockCode'] == col['StockCode'])
                  & (customer_data['InvoiceDate'] < col['InvoiceDate']) & (customer_data['Quantity'] > 0)].copy()
    #**********************************************
    #Cancellation without counterpart
    if (data_test.shape[0] == 0):
        doubtful_entry.append(index)
        
    #Cancelation with a counterpart
    elif (data_test.shape[0] == 1):
        counterpart_index = data_test.index[0]
        data_cleaned.loc[counterpart_index, 'QuantityCanceled'] = -col['Quantity']
        entry_to_remove.append(index)
        
    #Entries with several counterparts. We delete the last one
    elif (data_test.shape[0]>1):
        data_test.sort_index(axis=0, ascending=False, inplace=True)
        for ind, val in data_test.iterrows():
            if val['Quantity'] < -col['Quantity']: continue
            data_cleaned.loc[ind, 'QuantityCanceled'] = -col['Quantity']
            entry_to_remove.append(index)
            break


print("Length of cancelled orders without a counterpart and ones with one or more counterpart:", len(doubtful_entry + entry_to_remove))

Length of cancelled orders without a counterpart and ones with one or more counterpart: 9771


#Remove entries that do not have a counterpart and ones that have atleast one counterpart
data_cleaned.drop(entry_to_remove, axis=0, inplace=True)
data_cleaned.drop(doubtful_entry, axis=0, inplace=True)
print("Done!")

Done!


#Check for entries that have  negative quantity
remaining_entries = data_cleaned[(data_cleaned['Quantity']<0) & (data_cleaned['StockCode']!='D')]
print("Remaining entries to delete: {}".format(remaining_entries.shape[0]))
remaining_entries[:5]

Remaining entries to delete: 739


#Remove remaining supsicious entries
data_cleaned.drop(remaining_entries.index,axis=0,inplace=True)
print("Done!")

Done!


print("Number of entries to delete: {}".format(data_cleaned[(data_cleaned['Quantity']<0) & (data_cleaned['StockCode']!='D')].shape[0]))
remaining_entries[:5]
print("Length of data frame now: {}", len(data_cleaned))

Number of entries to delete: 0
Length of data frame now: {} 526131


#Compute total amount for each entry
data_cleaned['TotalPrice'] = data_cleaned['UnitPrice'] * (data_cleaned['Quantity'] - data_cleaned['QuantityCanceled'])
data_cleaned['TotalQuantity'] = data_cleaned['Quantity'] - data_cleaned['QuantityCanceled']
data_cleaned.sort_values('CustomerID')[:5]


#Sum of purchases/ user & order
order_total_df = data_cleaned.groupby(by= ['CustomerID', 'InvoiceNo', 'InvoiceDate'], as_index=False)['TotalPrice', 'TotalQuantity'].sum()
basket_price = order_total_df.rename(columns= {'TotalPrice' :'Basket Price'})

#Selection of important entries
basket_price=basket_price[basket_price['Basket Price']>0]
basket_price.sort_values('CustomerID')[:6]

C:\Users\glori\AppData\Local\Temp\ipykernel_10432\2682052397.py:2: FutureWarning:

Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.


basket_price.loc[basket_price['Basket Price'].idxmax()]
price_range = [0, 50, 100, 200, 500, 1000, 5000, 50000]
count_price = []
for i, price in enumerate(price_range):
  if i==0: continue
  items_count = basket_price[(basket_price['Basket Price'] < price) & 
                     (basket_price['Basket Price']>price_range[i-1])]['Basket Price'].count()
  count_price.append(items_count)
#*******************************************************************
#Representation of the purchases amount
plt.rc('font', weight='bold')
f, ax = plt.subplots(figsize=(10,7))
labels = ['{}<.<{}'.format(price_range[i-1], s) for i,s in enumerate(price_range) if i!=0]
sizes = count_price
explode = [0.0 if sizes[i] < 100 else 0.0 for i in range(len(sizes))]
ax.pie(sizes, explode = explode, labels=labels, autopct = lambda x:'{:1.0f}%'.format(x)
if x>1 else '', shadow =False, startangle=0)
ax.axis('equal')
f.text(0.5, 1.01, "Representation of the purchases amount",
       ha = 'center', fontsize = 18);


#Compute how recent a single customer performed a transaction
df_recency = basket_price.groupby(by='CustomerID',
                        as_index=False)['InvoiceDate'].max()
df_recency.columns = ['CustomerID', 'LastPurchaseDate']
recent_date = df_recency['LastPurchaseDate'].max()
df_recency['Recency'] = df_recency['LastPurchaseDate'].apply(
    lambda x: (recent_date - x).days)
df_recency


#Compute the number of times a customer has made a transaction
frequency_df = basket_price.groupby(
    by=['CustomerID'], as_index=False)['InvoiceDate'].count()
frequency_df.columns = ['CustomerID', 'Frequency']
frequency_df.head()


#Compute the total amount spent by a customer within the given time. 
monetary_df = basket_price.groupby(by='CustomerID', as_index=False)['Basket Price'].sum()
monetary_df.columns = ['CustomerID', 'Monetary']
monetary_df.head()


#Generate the RFM data set
rf_df = df_recency.merge(frequency_df, on='CustomerID')
rfm_df = rf_df.merge(monetary_df, on='CustomerID').drop(
    columns='LastPurchaseDate')
rfm_df.head()


plt.figure(figsize=(12,12))
plt.title("RFM variables distribution")
rfm_df.boxplot()

<AxesSubplot:title={'center':'RFM variables distribution'}>


#Data normalization
print("Scaling data....")
rfm_normalized = rfm_df[['Recency', 'Frequency', 'Monetary']]

scaler = StandardScaler()

scaler.fit(rfm_normalized)

rfm_normalized = pd.DataFrame(scaler.transform(rfm_normalized), columns=rfm_normalized.columns)
print("Done!")

Scaling data....
Done!


rfm_normalized


#Check for outliers after scaling 
plt.figure(figsize=(12,12))
plt.title("Outlier variable distribution")
rfm_normalized.boxplot()

<AxesSubplot:title={'center':'Outlier variable distribution'}>


#Outlier information
rfm_normalized[rfm_normalized['Monetary']>60]


#Remove outlier information
print()
indexID = rfm_normalized[rfm_normalized['Monetary'] > 60].index
rfm_normalized.drop(indexID, inplace=True)
print("Done!")

Done!


# Recheck the distribution of data
plt.figure(figsize=(12,12))
plt.title("RFM variable distribution")
rfm_normalized.boxplot()

<AxesSubplot:title={'center':'RFM variable distribution'}>


#Compute the optimal numer of clusters by 
inertia = []
num_clusters =range(2,8)
for i in num_clusters:
    kmeans=KMeans(n_clusters=i, max_iter=50)
    kmeans.fit(rfm_normalized)
    inertia.append(kmeans.inertia_)
    
plt.figure(figsize=(16,8))
plt.plot(num_clusters, inertia, 'bx-')
plt.xlabel('Number of clusters')
plt.ylabel('inertia')
plt.title('The Elbow Method showing the optimal k')
plt.show()
print()


#Silhouette analysis
print("Calculating silhouette scores for different values of k.....")
for k in num_clusters:
    #initialize kmeans
    kmeans = KMeans(n_clusters=k, max_iter=50)
    kmeans.fit(rfm_normalized)
    cluster_labels = kmeans.labels_
    
    #silhouette score
    silhouette_avg = silhouette_score(rfm_normalized, cluster_labels)
    print("For n_clusters={0}, the silhouette score is {1}".format(k, silhouette_avg))

Calculating silhouette scores for different values of k.....
For n_clusters=2, the silhouette score is 0.6873406120911523
For n_clusters=3, the silhouette score is 0.5845143260289866
For n_clusters=4, the silhouette score is 0.6030597736942671
For n_clusters=5, the silhouette score is 0.492318872510545
For n_clusters=6, the silhouette score is 0.5060299893063445
For n_clusters=7, the silhouette score is 0.5185848606534478


#Visulaization of silhoutte analysis 
print("Visulization of silhouette scores against clustered data for different values of k")
for i, k in enumerate([2, 3, 4, 5, 6]):
    fig, (ax1, ax2) = plt.subplots(1, 2,figsize=(15,5))
    #fig.set_size_inches(15, 5)
    
    # Run the Kmeans algorithm
    km=KMeans(n_clusters=k)
    labels = km.fit_predict(rfm_normalized)
    centroids = km.cluster_centers_
    

    # Get silhouette samples
    silhouette_vals = silhouette_samples(rfm_normalized, labels)

    # Silhouette plot
    y_ticks = []
    y_lower, y_upper = 0, 0
    for i, cluster in enumerate(np.unique(labels)):
        cluster_silhouette_vals = silhouette_vals[labels == cluster]
        cluster_silhouette_vals.sort()
        y_upper += len(cluster_silhouette_vals)
        ax1.barh(range(y_lower, y_upper), cluster_silhouette_vals, edgecolor='none', height=1)
        ax1.text(-0.03, (y_lower + y_upper) / 2, str(i + 1))
        y_lower += len(cluster_silhouette_vals)

    # Get the average silhouette score and plot it
    avg_score = np.mean(silhouette_vals)
    ax1.axvline(avg_score, linestyle='--', linewidth=2, color='green')
    ax1.set_yticks([])
    ax1.set_xlim([-0.1, 1])
    ax1.set_xlabel('Silhouette coefficient values')
    ax1.set_ylabel('Cluster labels')
    ax1.set_title('Silhouette plot for the various clusters', y=1.02);
    
    # Scatter plot of data colored with labels
    ax2.scatter(rfm_normalized['Monetary'], rfm_normalized['Recency'], c=labels)
    ax2.scatter(centroids[:, 0], centroids[:, 1], marker='*', c='r', s=250)
    # ax2.set_xlim([-2, 2])
    # ax2.set_xlim([-2, 2])
    ax2.set_xlabel('Monetary value')
    ax2.set_ylabel('Recency')
    ax2.set_title('Visualization of clustered data', y=1.02)
    ax2.set_aspect('equal')
    plt.tight_layout()
    plt.suptitle(f'Silhouette analysis using k = {k}',
                 fontsize=16, fontweight='semibold', y=1.05);

Visulization of silhouette scores against clustered data for different values of k


#Segmentation of customers
kmeans = KMeans(n_clusters=4, max_iter=50)
kmeans.fit(rfm_normalized)

KMeans(max_iter=50, n_clusters=4)

KMeans(max_iter=50, n_clusters=4)


rfm_normalized.loc[:, 'CustomerID'] = rfm_df['CustomerID']


rfm_normalized


rfm_normalized['Cluster'] = kmeans.labels_
rfm_normalized


rfm_normalized['Cluster'].value_counts()

0    2875
2     803
1     626
3      23
Name: Cluster, dtype: int64


plt.figure(figsize=(12,12))
plt.title("Recency variable distribution within each cluster")
sns.boxplot(x='Cluster', y='Recency', data=rfm_normalized)

<AxesSubplot:title={'center':'Recency variable distribution within each cluster'}, xlabel='Cluster', ylabel='Recency'>


plt.figure(figsize=(12,12))
plt.title("Frequency variable distribution within each cluster")
sns.boxplot(x='Cluster', y='Frequency', data=rfm_normalized)

<AxesSubplot:title={'center':'Frequency variable distribution within each cluster'}, xlabel='Cluster', ylabel='Frequency'>


plt.figure(figsize=(12,12))
plt.title("Monetary variable distribution within each cluster")
sns.boxplot(x='Cluster', y='Monetary', data=rfm_normalized)

<AxesSubplot:title={'center':'Monetary variable distribution within each cluster'}, xlabel='Cluster', ylabel='Monetary'>


plt.figure(figsize=(12,12))
plt.title("Clustering: Recency vs Monetary")
RM=sns.scatterplot(x='Recency', y='Monetary', hue='Cluster', palette="Set2", data=rfm_normalized)


plt.figure(figsize=(12,12))
plt.title("Clustering: Frequency vs Monetary")
FM=sns.scatterplot(x='Frequency', y='Monetary', hue='Cluster',palette="Set2", data=rfm_normalized)


plt.figure(figsize=(12,12))
plt.title("Clustering: Recency vs Frequency")
RF=sns.scatterplot(x='Recency', y='Frequency', hue='Cluster', palette="Set2", data=rfm_normalized)


plt.rcParams["figure.figsize"] = (25,25)
fig = plt.figure(1)
plt.clf()
ax = Axes3D(fig, rect = [0, 0, .95, 1],
            elev = 48,
            azim = 134)

plt.cla()
ax.scatter(rfm_normalized['Recency'], rfm_normalized['Frequency'], rfm_normalized['Monetary'],
           c = rfm_normalized['Cluster'],
           s = 200,
           cmap = "spring",
           alpha = 0.5,
           edgecolor = 'darkgrey')

ax.set_xlabel('Recency',
              fontsize = 16)
ax.set_ylabel('Frequency',
              fontsize = 16)
ax.set_zlabel('Monetary',
              fontsize = 16)

plt.show()
# import plotly.express as px

# fig2 = px.scatter_3d(rfm_normalized, x="Frequency", y="Recency", z="Monetary", color="Cluster",)
# fig2.update_layout(title="3 Features Representation")
# fig2.show()

C:\Users\glori\AppData\Local\Temp\ipykernel_10432\3530151970.py:4: MatplotlibDeprecationWarning:

Axes3D(fig) adding itself to the figure is deprecated since 3.4. Pass the keyword argument auto_add_to_figure=False and use fig.add_axes(ax) to suppress this warning. The default value of auto_add_to_figure will change to False in mpl3.5 and True values will no longer work in 3.6.  This is consistent with other Axes classes.


#Selection and distribution of independent and dependent variables
X = rfm_normalized.iloc[:,:3].values
X[:10]

array([[-9.05228987e-01,  1.07093859e-01,  7.28288079e-02],
       [-1.76467627e-01, -2.57655586e-02, -1.97274636e-02],
       [-7.35517437e-01, -1.58624976e-01, -2.11894251e-02],
       [ 2.16954497e+00, -1.58624976e-01, -7.36104514e-02],
       [-5.65805888e-01,  1.07093859e-01,  1.94853772e-03],
       [ 1.11134354e+00, -1.58624976e-01, -8.26496390e-02],
       [ 1.39086845e+00, -1.58624976e-01, -4.61687449e-02],
       [ 1.21117387e+00, -1.58624976e-01, -6.90061382e-02],
       [-6.95585308e-01, -7.00520310e-02,  1.76297233e-02],
       [-5.95754985e-01, -1.58624976e-01,  1.42728544e-01]])


Y = rfm_normalized.iloc[:,4:5].values
Y[:10]

array([[0],
       [0],
       [0],
       [1],
       [0],
       [2],
       [1],
       [2],
       [0],
       [0]])


#One hot encode
ohe = OneHotEncoder()
Y = ohe.fit_transform(Y).toarray()
print('One hot encoded array:')
print(Y[0:5])

One hot encoded array:
[[1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [1. 0. 0. 0.]
 [0. 1. 0. 0.]
 [1. 0. 0. 0.]]


#Train test split of model
X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size = 0.1,random_state = 0)
print("Done!")

Done!


#Defining the model
model = Sequential()


model.add(Dense(X_train.shape[1], input_dim=X_train.shape[1], activation='relu')) #Input layer with 16 units
model.add(Dense(4, activation='relu')) #Hidden layer with 12 units
model.add(Dense(4, activation='softmax'))#Middle layer with 4 units


model.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 dense (Dense)               (None, 3)                 12        
                                                                 
 dense_1 (Dense)             (None, 4)                 16        
                                                                 
 dense_2 (Dense)             (None, 4)                 20        
                                                                 
=================================================================
Total params: 48
Trainable params: 48
Non-trainable params: 0
_________________________________________________________________


model.layers[0].output

<KerasTensor: shape=(None, 3) dtype=float32 (created by layer 'dense')>


model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])


history = model.fit(X_train, Y_train, epochs=100, batch_size=64, validation_split = 0.1)

Epoch 1/100
55/55 [==============================] - 1s 4ms/step - loss: 1.3834 - accuracy: 0.2446 - val_loss: 1.3150 - val_accuracy: 0.3872
Epoch 2/100
55/55 [==============================] - 0s 2ms/step - loss: 1.2735 - accuracy: 0.5950 - val_loss: 1.2236 - val_accuracy: 0.8077
Epoch 3/100
55/55 [==============================] - 0s 2ms/step - loss: 1.1934 - accuracy: 0.8051 - val_loss: 1.1491 - val_accuracy: 0.8154
Epoch 4/100
55/55 [==============================] - 0s 2ms/step - loss: 1.1231 - accuracy: 0.8074 - val_loss: 1.0802 - val_accuracy: 0.8179
Epoch 5/100
55/55 [==============================] - 0s 2ms/step - loss: 1.0570 - accuracy: 0.8085 - val_loss: 1.0150 - val_accuracy: 0.8205
Epoch 6/100
55/55 [==============================] - 0s 1ms/step - loss: 0.9941 - accuracy: 0.8088 - val_loss: 0.9537 - val_accuracy: 0.8205
Epoch 7/100
55/55 [==============================] - 0s 2ms/step - loss: 0.9353 - accuracy: 0.8111 - val_loss: 0.8971 - val_accuracy: 0.8308
Epoch 8/100
55/55 [==============================] - 0s 2ms/step - loss: 0.8806 - accuracy: 0.8225 - val_loss: 0.8450 - val_accuracy: 0.8462
Epoch 9/100
55/55 [==============================] - 0s 2ms/step - loss: 0.8307 - accuracy: 0.8459 - val_loss: 0.7978 - val_accuracy: 0.8590
Epoch 10/100
55/55 [==============================] - 0s 1ms/step - loss: 0.7851 - accuracy: 0.8527 - val_loss: 0.7550 - val_accuracy: 0.8692
Epoch 11/100
55/55 [==============================] - 0s 2ms/step - loss: 0.7435 - accuracy: 0.8664 - val_loss: 0.7157 - val_accuracy: 0.8744
Epoch 12/100
55/55 [==============================] - 0s 1ms/step - loss: 0.7053 - accuracy: 0.8710 - val_loss: 0.6795 - val_accuracy: 0.8769
Epoch 13/100
55/55 [==============================] - 0s 2ms/step - loss: 0.6700 - accuracy: 0.8821 - val_loss: 0.6459 - val_accuracy: 0.8769
Epoch 14/100
55/55 [==============================] - 0s 2ms/step - loss: 0.6371 - accuracy: 0.8881 - val_loss: 0.6147 - val_accuracy: 0.8897
Epoch 15/100
55/55 [==============================] - 0s 2ms/step - loss: 0.6063 - accuracy: 0.8993 - val_loss: 0.5852 - val_accuracy: 0.8923
Epoch 16/100
55/55 [==============================] - 0s 2ms/step - loss: 0.5774 - accuracy: 0.9075 - val_loss: 0.5574 - val_accuracy: 0.9103
Epoch 17/100
55/55 [==============================] - 0s 2ms/step - loss: 0.5500 - accuracy: 0.9150 - val_loss: 0.5309 - val_accuracy: 0.9103
Epoch 18/100
55/55 [==============================] - 0s 2ms/step - loss: 0.5238 - accuracy: 0.9232 - val_loss: 0.5060 - val_accuracy: 0.9103
Epoch 19/100
55/55 [==============================] - 0s 2ms/step - loss: 0.4988 - accuracy: 0.9289 - val_loss: 0.4818 - val_accuracy: 0.9282
Epoch 20/100
55/55 [==============================] - 0s 2ms/step - loss: 0.4748 - accuracy: 0.9386 - val_loss: 0.4585 - val_accuracy: 0.9333
Epoch 21/100
55/55 [==============================] - 0s 2ms/step - loss: 0.4518 - accuracy: 0.9449 - val_loss: 0.4362 - val_accuracy: 0.9410
Epoch 22/100
55/55 [==============================] - 0s 1ms/step - loss: 0.4299 - accuracy: 0.9521 - val_loss: 0.4147 - val_accuracy: 0.9385
Epoch 23/100
55/55 [==============================] - 0s 2ms/step - loss: 0.4082 - accuracy: 0.9609 - val_loss: 0.3944 - val_accuracy: 0.9410
Epoch 24/100
55/55 [==============================] - 0s 2ms/step - loss: 0.3876 - accuracy: 0.9626 - val_loss: 0.3745 - val_accuracy: 0.9538
Epoch 25/100
55/55 [==============================] - 0s 2ms/step - loss: 0.3682 - accuracy: 0.9666 - val_loss: 0.3559 - val_accuracy: 0.9615
Epoch 26/100
55/55 [==============================] - 0s 1ms/step - loss: 0.3498 - accuracy: 0.9689 - val_loss: 0.3385 - val_accuracy: 0.9590
Epoch 27/100
55/55 [==============================] - 0s 2ms/step - loss: 0.3326 - accuracy: 0.9729 - val_loss: 0.3220 - val_accuracy: 0.9641
Epoch 28/100
55/55 [==============================] - 0s 2ms/step - loss: 0.3167 - accuracy: 0.9746 - val_loss: 0.3065 - val_accuracy: 0.9692
Epoch 29/100
55/55 [==============================] - 0s 2ms/step - loss: 0.3015 - accuracy: 0.9737 - val_loss: 0.2925 - val_accuracy: 0.9641
Epoch 30/100
55/55 [==============================] - 0s 2ms/step - loss: 0.2874 - accuracy: 0.9789 - val_loss: 0.2788 - val_accuracy: 0.9744
Epoch 31/100
55/55 [==============================] - 0s 2ms/step - loss: 0.2742 - accuracy: 0.9812 - val_loss: 0.2663 - val_accuracy: 0.9744
Epoch 32/100
55/55 [==============================] - 0s 2ms/step - loss: 0.2622 - accuracy: 0.9809 - val_loss: 0.2545 - val_accuracy: 0.9821
Epoch 33/100
55/55 [==============================] - 0s 2ms/step - loss: 0.2508 - accuracy: 0.9814 - val_loss: 0.2434 - val_accuracy: 0.9821
Epoch 34/100
55/55 [==============================] - 0s 2ms/step - loss: 0.2404 - accuracy: 0.9812 - val_loss: 0.2331 - val_accuracy: 0.9846
Epoch 35/100
55/55 [==============================] - 0s 2ms/step - loss: 0.2304 - accuracy: 0.9840 - val_loss: 0.2238 - val_accuracy: 0.9872
Epoch 36/100
55/55 [==============================] - 0s 2ms/step - loss: 0.2211 - accuracy: 0.9834 - val_loss: 0.2147 - val_accuracy: 0.9872
Epoch 37/100
55/55 [==============================] - 0s 1ms/step - loss: 0.2128 - accuracy: 0.9857 - val_loss: 0.2069 - val_accuracy: 0.9872
Epoch 38/100
55/55 [==============================] - 0s 2ms/step - loss: 0.2048 - accuracy: 0.9832 - val_loss: 0.1987 - val_accuracy: 0.9897
Epoch 39/100
55/55 [==============================] - 0s 2ms/step - loss: 0.1975 - accuracy: 0.9840 - val_loss: 0.1928 - val_accuracy: 0.9795
Epoch 40/100
55/55 [==============================] - 0s 2ms/step - loss: 0.1905 - accuracy: 0.9857 - val_loss: 0.1853 - val_accuracy: 0.9872
Epoch 41/100
55/55 [==============================] - 0s 2ms/step - loss: 0.1840 - accuracy: 0.9863 - val_loss: 0.1789 - val_accuracy: 0.9872
Epoch 42/100
55/55 [==============================] - 0s 2ms/step - loss: 0.1779 - accuracy: 0.9860 - val_loss: 0.1723 - val_accuracy: 0.9974
Epoch 43/100
55/55 [==============================] - 0s 2ms/step - loss: 0.1723 - accuracy: 0.9857 - val_loss: 0.1670 - val_accuracy: 0.9872
Epoch 44/100
55/55 [==============================] - 0s 1ms/step - loss: 0.1668 - accuracy: 0.9846 - val_loss: 0.1617 - val_accuracy: 0.9923
Epoch 45/100
55/55 [==============================] - 0s 2ms/step - loss: 0.1619 - accuracy: 0.9874 - val_loss: 0.1565 - val_accuracy: 0.9949
Epoch 46/100
55/55 [==============================] - 0s 2ms/step - loss: 0.1573 - accuracy: 0.9852 - val_loss: 0.1519 - val_accuracy: 0.9974
Epoch 47/100
55/55 [==============================] - 0s 2ms/step - loss: 0.1525 - accuracy: 0.9860 - val_loss: 0.1479 - val_accuracy: 0.9949
Epoch 48/100
55/55 [==============================] - 0s 2ms/step - loss: 0.1485 - accuracy: 0.9854 - val_loss: 0.1437 - val_accuracy: 0.9949
Epoch 49/100
55/55 [==============================] - 0s 1ms/step - loss: 0.1444 - accuracy: 0.9874 - val_loss: 0.1398 - val_accuracy: 0.9923
Epoch 50/100
55/55 [==============================] - 0s 1ms/step - loss: 0.1407 - accuracy: 0.9860 - val_loss: 0.1350 - val_accuracy: 0.9974
Epoch 51/100
55/55 [==============================] - 0s 2ms/step - loss: 0.1371 - accuracy: 0.9869 - val_loss: 0.1322 - val_accuracy: 0.9949
Epoch 52/100
55/55 [==============================] - 0s 2ms/step - loss: 0.1334 - accuracy: 0.9869 - val_loss: 0.1282 - val_accuracy: 0.9949
Epoch 53/100
55/55 [==============================] - 0s 2ms/step - loss: 0.1302 - accuracy: 0.9869 - val_loss: 0.1248 - val_accuracy: 0.9974
Epoch 54/100
55/55 [==============================] - 0s 2ms/step - loss: 0.1272 - accuracy: 0.9892 - val_loss: 0.1217 - val_accuracy: 0.9974
Epoch 55/100
55/55 [==============================] - 0s 1ms/step - loss: 0.1242 - accuracy: 0.9869 - val_loss: 0.1195 - val_accuracy: 0.9949
Epoch 56/100
55/55 [==============================] - 0s 1ms/step - loss: 0.1215 - accuracy: 0.9874 - val_loss: 0.1159 - val_accuracy: 0.9949
Epoch 57/100
55/55 [==============================] - 0s 1ms/step - loss: 0.1189 - accuracy: 0.9883 - val_loss: 0.1133 - val_accuracy: 0.9974
Epoch 58/100
55/55 [==============================] - 0s 1ms/step - loss: 0.1163 - accuracy: 0.9883 - val_loss: 0.1107 - val_accuracy: 0.9974
Epoch 59/100
55/55 [==============================] - 0s 1ms/step - loss: 0.1138 - accuracy: 0.9883 - val_loss: 0.1085 - val_accuracy: 0.9974
Epoch 60/100
55/55 [==============================] - 0s 1ms/step - loss: 0.1115 - accuracy: 0.9886 - val_loss: 0.1056 - val_accuracy: 0.9923
Epoch 61/100
55/55 [==============================] - 0s 1ms/step - loss: 0.1097 - accuracy: 0.9877 - val_loss: 0.1034 - val_accuracy: 0.9974
Epoch 62/100
55/55 [==============================] - 0s 1ms/step - loss: 0.1074 - accuracy: 0.9892 - val_loss: 0.1013 - val_accuracy: 0.9974
Epoch 63/100
55/55 [==============================] - 0s 1ms/step - loss: 0.1053 - accuracy: 0.9889 - val_loss: 0.0999 - val_accuracy: 0.9974
Epoch 64/100
55/55 [==============================] - 0s 1ms/step - loss: 0.1035 - accuracy: 0.9889 - val_loss: 0.0981 - val_accuracy: 0.9949
Epoch 65/100
55/55 [==============================] - 0s 1ms/step - loss: 0.1015 - accuracy: 0.9880 - val_loss: 0.0954 - val_accuracy: 0.9949
Epoch 66/100
55/55 [==============================] - 0s 1ms/step - loss: 0.1002 - accuracy: 0.9872 - val_loss: 0.0937 - val_accuracy: 0.9974
Epoch 67/100
55/55 [==============================] - 0s 1ms/step - loss: 0.0984 - accuracy: 0.9872 - val_loss: 0.0914 - val_accuracy: 0.9923
Epoch 68/100
55/55 [==============================] - 0s 1ms/step - loss: 0.0968 - accuracy: 0.9872 - val_loss: 0.0903 - val_accuracy: 0.9974
Epoch 69/100
55/55 [==============================] - 0s 1ms/step - loss: 0.0953 - accuracy: 0.9883 - val_loss: 0.0883 - val_accuracy: 0.9923
Epoch 70/100
55/55 [==============================] - 0s 1ms/step - loss: 0.0937 - accuracy: 0.9883 - val_loss: 0.0871 - val_accuracy: 0.9974
Epoch 71/100
55/55 [==============================] - 0s 1ms/step - loss: 0.0927 - accuracy: 0.9857 - val_loss: 0.0857 - val_accuracy: 0.9974
Epoch 72/100
55/55 [==============================] - 0s 1ms/step - loss: 0.0909 - accuracy: 0.9874 - val_loss: 0.0843 - val_accuracy: 0.9974
Epoch 73/100
55/55 [==============================] - 0s 1ms/step - loss: 0.0898 - accuracy: 0.9880 - val_loss: 0.0828 - val_accuracy: 0.9974
Epoch 74/100
55/55 [==============================] - 0s 1ms/step - loss: 0.0886 - accuracy: 0.9883 - val_loss: 0.0826 - val_accuracy: 0.9974
Epoch 75/100
55/55 [==============================] - 0s 1ms/step - loss: 0.0872 - accuracy: 0.9883 - val_loss: 0.0805 - val_accuracy: 0.9974
Epoch 76/100
55/55 [==============================] - 0s 1ms/step - loss: 0.0860 - accuracy: 0.9880 - val_loss: 0.0792 - val_accuracy: 0.9974
Epoch 77/100
55/55 [==============================] - 0s 1ms/step - loss: 0.0850 - accuracy: 0.9897 - val_loss: 0.0787 - val_accuracy: 0.9974
Epoch 78/100
55/55 [==============================] - 0s 1ms/step - loss: 0.0839 - accuracy: 0.9877 - val_loss: 0.0769 - val_accuracy: 0.9974
Epoch 79/100
55/55 [==============================] - 0s 1ms/step - loss: 0.0830 - accuracy: 0.9894 - val_loss: 0.0762 - val_accuracy: 0.9974
Epoch 80/100
55/55 [==============================] - 0s 1ms/step - loss: 0.0822 - accuracy: 0.9877 - val_loss: 0.0747 - val_accuracy: 0.9974
Epoch 81/100
55/55 [==============================] - 0s 1ms/step - loss: 0.0808 - accuracy: 0.9892 - val_loss: 0.0733 - val_accuracy: 0.9974
Epoch 82/100
55/55 [==============================] - 0s 1ms/step - loss: 0.0799 - accuracy: 0.9897 - val_loss: 0.0722 - val_accuracy: 0.9974
Epoch 83/100
55/55 [==============================] - 0s 1ms/step - loss: 0.0790 - accuracy: 0.9880 - val_loss: 0.0722 - val_accuracy: 0.9974
Epoch 84/100
55/55 [==============================] - 0s 1ms/step - loss: 0.0782 - accuracy: 0.9889 - val_loss: 0.0705 - val_accuracy: 0.9974
Epoch 85/100
55/55 [==============================] - 0s 1ms/step - loss: 0.0775 - accuracy: 0.9894 - val_loss: 0.0704 - val_accuracy: 0.9974
Epoch 86/100
55/55 [==============================] - 0s 1ms/step - loss: 0.0765 - accuracy: 0.9889 - val_loss: 0.0683 - val_accuracy: 0.9974
Epoch 87/100
55/55 [==============================] - 0s 1ms/step - loss: 0.0754 - accuracy: 0.9892 - val_loss: 0.0672 - val_accuracy: 0.9974
Epoch 88/100
55/55 [==============================] - 0s 1ms/step - loss: 0.0747 - accuracy: 0.9892 - val_loss: 0.0661 - val_accuracy: 0.9974
Epoch 89/100
55/55 [==============================] - 0s 1ms/step - loss: 0.0739 - accuracy: 0.9900 - val_loss: 0.0653 - val_accuracy: 0.9974
Epoch 90/100
55/55 [==============================] - 0s 1ms/step - loss: 0.0729 - accuracy: 0.9909 - val_loss: 0.0648 - val_accuracy: 0.9974
Epoch 91/100
55/55 [==============================] - 0s 1ms/step - loss: 0.0721 - accuracy: 0.9903 - val_loss: 0.0638 - val_accuracy: 0.9974
Epoch 92/100
55/55 [==============================] - 0s 2ms/step - loss: 0.0714 - accuracy: 0.9903 - val_loss: 0.0635 - val_accuracy: 0.9974
Epoch 93/100
55/55 [==============================] - 0s 1ms/step - loss: 0.0707 - accuracy: 0.9897 - val_loss: 0.0612 - val_accuracy: 0.9923
Epoch 94/100
55/55 [==============================] - 0s 2ms/step - loss: 0.0698 - accuracy: 0.9900 - val_loss: 0.0613 - val_accuracy: 0.9974
Epoch 95/100
55/55 [==============================] - 0s 1ms/step - loss: 0.0687 - accuracy: 0.9914 - val_loss: 0.0599 - val_accuracy: 0.9974
Epoch 96/100
55/55 [==============================] - 0s 1ms/step - loss: 0.0682 - accuracy: 0.9900 - val_loss: 0.0588 - val_accuracy: 0.9923
Epoch 97/100
55/55 [==============================] - 0s 1ms/step - loss: 0.0671 - accuracy: 0.9903 - val_loss: 0.0582 - val_accuracy: 0.9974
Epoch 98/100
55/55 [==============================] - 0s 1ms/step - loss: 0.0661 - accuracy: 0.9914 - val_loss: 0.0566 - val_accuracy: 0.9949
Epoch 99/100
55/55 [==============================] - 0s 1ms/step - loss: 0.0651 - accuracy: 0.9914 - val_loss: 0.0557 - val_accuracy: 0.9974
Epoch 100/100
55/55 [==============================] - 0s 1ms/step - loss: 0.0639 - accuracy: 0.9900 - val_loss: 0.0548 - val_accuracy: 0.9974


print("Predicting.....")
y_pred = model.predict(X_test)
#Converting predictions to label
pred = list()
for i in range(len(y_pred)):
    pred.append(np.argmax(y_pred[i]))
print("Done!")

Predicting.....
14/14 [==============================] - 0s 694us/step
Done!


#Converting one hot encoded test label to label
test = list()
for i in range(len(Y_test)):
    test.append(np.argmax(Y_test[i]))
print("Done!")

Done!


print()
print("Pridicted values")
pred[:10]

Pridicted values

[2, 0, 1, 2, 0, 0, 2, 0, 0, 0]


print()
print("Actual values")
test[:10]

Actual values

[2, 0, 1, 2, 0, 0, 2, 0, 0, 0]


a = accuracy_score(pred,test)
print('Accuracy of model on test data is:', a*100)

Accuracy of model on test data is: 99.76905311778292


plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Val'], loc='upper left')
plt.show()


plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Val'], loc='upper left')
plt.show()


# print("Saving model....")
# model.save('./Final_outputs/model.h5');
# print("Model saved")


#Function to predict customer group from user input
def run_model(Recency_pred, Frequency_pred, Monetary_pred):
    pred_data = {'Recency':[Recency_pred], 'Frequency':[Frequency_pred], 'Monetary':[Monetary_pred] }
    pred_data=pd.DataFrame(pred_data)
    print()
    print("UNSCALED DATA SET")
    print(pred_data)
    pred_data = pd.DataFrame(scaler.transform(pred_data), columns=pred_data.columns)
    print()
    print("SCALED DATA SET")
    print(pred_data)
    print()
    print("PREDICTING......")
    X1=np.array(pred_data)
    print(X1)
    result = model.predict(X1)
    k = np.argmax(result)
    out = Output(layout={'border': '1px solid white'})

    with out:
        print(k)

    return out


interact(run_model, Recency_pred = BoundedFloatText(value = 0, min = 0, max=373, step = 1, description = 'Recency'),
Frequency_pred = BoundedFloatText(value = 0, min = 1, step = 1, max=1402, description = 'Frequency'),
Monetary_pred = BoundedFloatText(value = 2.9, min = 2.9, step    = 1,max=279765.02,  description = 'Monetary'),)

interactive(children=(BoundedFloatText(value=0.0, description='Recency', max=373.0, step=1.0), BoundedFloatTex…

<function __main__.run_model(Recency_pred, Frequency_pred, Monetary_pred)>

	CustomerID	InvoiceNo	Number of products
0	12346.0	541431	1
1	12346.0	C541433	1
2	12347.0	537626	31
3	12347.0	542237	29
4	12347.0	549222	24
5	12347.0	556201	18
6	12347.0	562032	22
7	12347.0	573511	47
8	12347.0	581180	11
9	12348.0	539318	17

	InvoiceNo	StockCode	Description	Quantity	InvoiceDate	UnitPrice	CustomerID	Country
299982	A563185	B	Adjust bad debt	1	2011-08-12 14:50:00	11062.06	NaN	United Kingdom
299983	A563186	B	Adjust bad debt	1	2011-08-12 14:51:00	-11062.06	NaN	United Kingdom
299984	A563187	B	Adjust bad debt	1	2011-08-12 14:52:00	-11062.06	NaN	United Kingdom

	InvoiceNo	StockCode	Description	Quantity	InvoiceDate	UnitPrice	CustomerID	Country
61619	541431	23166	MEDIUM CERAMIC TOP STORAGE JAR	74215	2011-01-18 10:01:00	1.04	12346.0	United Kingdom
61624	C541433	23166	MEDIUM CERAMIC TOP STORAGE JAR	-74215	2011-01-18 10:17:00	1.04	12346.0	United Kingdom
428981	573511	22992	REVOLVER WOODEN RULER	12	2011-10-31 12:25:00	1.95	12347.0	Iceland
429001	573511	20719	WOODLAND CHARLOTTE BAG	10	2011-10-31 12:25:00	0.85	12347.0	Iceland
429002	573511	23162	REGENCY TEA STRAINER	8	2011-10-31 12:25:00	3.75	12347.0	Iceland

	InvoiceNo	StockCode	Description	Quantity	InvoiceDate	UnitPrice	CustomerID	Country	QuantityCanceled	TotalPrice	TotalQuantity
61619	541431	23166	MEDIUM CERAMIC TOP STORAGE JAR	74215	2011-01-18 10:01:00	1.04	12346.0	United Kingdom	74215	0.00	0
535014	581180	23508	MINI PLAYING CARDS DOLLY GIRL	20	2011-12-07 15:52:00	0.42	12347.0	Iceland	0	8.40	20
535011	581180	21265	PINK GOOSE FEATHER TREE 60CM	12	2011-12-07 15:52:00	1.95	12347.0	Iceland	0	23.40	12
14968	537626	20782	CAMOUFLAGE EAR MUFF HEADPHONES	6	2010-12-07 14:57:00	5.49	12347.0	Iceland	0	32.94	6
286621	562032	23308	SET OF 60 VINTAGE LEAF CAKE CASES	24	2011-08-02 08:48:00	0.55	12347.0	Iceland	0	13.20	24

	CustomerID	InvoiceNo	InvoiceDate	Basket Price	TotalQuantity
1	12347.0	537626	2010-12-07 14:57:00	711.79	319
2	12347.0	542237	2011-01-26 14:30:00	475.39	315
3	12347.0	549222	2011-04-07 10:43:00	636.25	483
4	12347.0	556201	2011-06-09 13:01:00	382.52	196
5	12347.0	562032	2011-08-02 08:48:00	584.91	277
6	12347.0	573511	2011-10-31 12:25:00	1294.32	676

	InvoiceNo	StockCode	Description	Quantity	InvoiceDate	UnitPrice	CustomerID	Country
141	C536379	D	Discount	-1	2010-12-01 09:41:00	27.50	14527.0	United Kingdom
154	C536383	35004C	SET OF 3 COLOURED FLYING DUCKS	-1	2010-12-01 09:49:00	4.65	15311.0	United Kingdom
235	C536391	22556	PLASTERS IN TIN CIRCUS PARADE	-12	2010-12-01 10:24:00	1.65	17548.0	United Kingdom
236	C536391	21984	PACK OF 12 PINK PAISLEY TISSUES	-24	2010-12-01 10:24:00	0.29	17548.0	United Kingdom
237	C536391	21983	PACK OF 12 BLUE PAISLEY TISSUES	-24	2010-12-01 10:24:00	0.29	17548.0	United Kingdom

	InvoiceNo	StockCode	Quantity	InvoiceDate	CustomerID	Country
7188	536996	22712	-20	2010-12-03 15:30:00	15287.69057	United Kingdom
7201	537009	84534B	-80	2010-12-03 15:38:00	15287.69057	United Kingdom
7202	537010	22162	-40	2010-12-03 15:38:00	15287.69057	United Kingdom
7205	537013	35965	-25	2010-12-03 15:40:00	15287.69057	United Kingdom
7291	537027	18098C	-140	2010-12-03 16:36:00	15287.69057	United Kingdom

	CustomerID	Monetary
0	12347.0	4310.00
1	12348.0	1797.24
2	12349.0	1757.55
3	12350.0	334.40
4	12352.0	2385.71

	Recency	Frequency	Monetary
0	-0.905229	0.107094	0.072829
1	-0.176468	-0.025766	-0.019727
2	-0.735517	-0.158625	-0.021189
3	2.169545	-0.158625	-0.073610
4	-0.565806	0.107094	0.001949
...	...	...	...
4323	1.850088	-0.158625	-0.079276
4324	0.881734	-0.158625	-0.082951
4325	-0.845331	-0.114339	-0.079423
4326	-0.885263	0.505672	-0.010582
4327	-0.495925	-0.070052	-0.018253

	CustomerID	LastPurchaseDate	Recency
0	12347.0	2011-12-07 15:52:00	1
1	12348.0	2011-09-25 13:13:00	74
2	12349.0	2011-11-21 09:51:00	18
3	12350.0	2011-02-02 16:01:00	309
4	12352.0	2011-11-03 14:37:00	35
...	...	...	...
4323	18280.0	2011-03-07 09:52:00	277
4324	18281.0	2011-06-12 10:53:00	180
4325	18282.0	2011-12-02 11:43:00	7
4326	18283.0	2011-12-06 12:02:00	3
4327	18287.0	2011-10-28 09:29:00	42

BEHAVIOURAL SEGMENTATION ¶

LOAD THE NECESSARY LIBRARIES ¶

DATA UNDERSTANDING¶

A. Load data¶

B. Summary of data set¶

C. Check for missing values.¶

Get the total number of missing values for each attribute¶

D. Check for duplicated entries¶

E. Countries¶

F. Customers and products¶

Total number of customers, products and transactions¶

Number of products purchased in every transaction¶

G. Stock codes¶

List of stock codes in the data set¶

Description of stock code 'B'¶

DATA PREPARATION¶

A. Dealing with missing values¶

Filling the missing values¶

Check if there are still missing values¶

B. Dealing with repeated entries¶

Remove duplicated entries¶

Check if there are still repeated entries¶

C. Remove bad orders¶

Gather all orders that might indicate a cancelled order¶

Check if negative quantity correspond to cancelled transaction¶

Check if all negative quantities correspond to cancelled orders¶

Check if all non discount negative quantities correspond to cancelled orders¶

Collect all transactions that relate to cancelled transactions¶

Remove all entries that do not have a counterpart and ones that have atleast one counterpart¶

Remove remaining non discount negative entries¶

Check if there are still negative non-discount entries to delete¶

D. Basket price¶

Compute the total amount for each entry¶

Compute the total amount for every transaction¶

Visualize the amount spent within transactions¶

E. RFM analysis¶

Compute how recent a customer performed a transaction¶

Compute the number of times a customer has made a transaction¶

Compute the total amount spent by a customer ¶

Generate the RFM data set¶

F. Outlier detection¶

G. Data Scaling¶

Scaled data¶

Check for outliers after scaling¶

Outlier Information¶

Remove outlier information¶

Recheck the distribution of data¶

MODELLING¶

A. Clustering.¶

Elbow method¶

Silhouette analysis¶

Segmentation¶

Add CustomerID column¶

Label every customers¶

2D Distribution of customers within clusters¶

Analyze the customer groups¶

3D representation¶

D. Artificial Neural Network¶

Sample of independent variables (RFM values)¶

Sample ependent variables(Cluster labels)¶

One hot encoding on the cluster labels¶

Split data set into training and testing data¶

Define the model¶

Training model¶

EVALUATION¶

Evaluate model on test data¶

Reverse one hot encoding process¶

Accuracy of mode on test data (%)¶

Visualize the accurracy and loss of training and validation data¶

Save model¶

PREDICTING NEW VALUES¶

Please input a users recency, frequency and monetary values¶